import plotly.graph_objects as go
import numpy as np
from filtering import load_data
from filtering import save_data
from filtering import apply_filter
# Lectura de datos crudos
data = load_data('PRUEBAS 24-03-2020/1-REPOSO/JUAN MANUEL/1585082913593.txt')
data
x = np.linspace(0, len(data), len(data) + 1)
y = data
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode='lines',
name='lines'
)
)
fig.update_layout(
title='Datos crudos',
xaxis_title=f'Rango en (0, {len(data)})',
yaxis_title='Dato recopilado'
)
fig.show()
# Aplicamos filtro pasa altos
hp_data = apply_filter(data, filter_name='high-pass')
hp_data
x = np.linspace(0, len(data), len(data) + 1)
y = hp_data
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode='lines',
name='lines'
)
)
fig.update_layout(
title='Datos con filtro pasa altas',
xaxis_title=f'Rango en (0, {len(data)})',
yaxis_title='Dato filtrado'
)
fig.show()
# Aplicamos filtro Notch de 60Hz
n60_data = apply_filter(hp_data, filter_name='notch-60')
n60_data
x = np.linspace(0, len(data), len(data) + 1)
y = n60_data
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode='lines',
name='lines'
)
)
fig.update_layout(
title='Datos con filtro Notch de 60Hz',
xaxis_title=f'Rango en (0, {len(data)})',
yaxis_title='Dato filtrado'
)
fig.show()
# Aplicamos filtro Notch de 120Hz
n120_data = apply_filter(n60_data, filter_name='notch-120')
n120_data
x = np.linspace(0, len(data), len(data) + 1)
y = n120_data
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode='lines',
name='lines'
)
)
fig.update_layout(
title='Datos con filtro Notch de 120Hz',
xaxis_title=f'Rango en (0, {len(data)})',
yaxis_title='Dato filtrado'
)
fig.show()
# Aplicamos filtro Notch de 180Hz
n180_data = apply_filter(n120_data, filter_name='notch-180')
n180_data
x = np.linspace(0, len(data), len(data) + 1)
y = n180_data
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode='lines',
name='lines'
)
)
fig.update_layout(
title='Datos con filtro Notch de 180Hz',
xaxis_title=f'Rango en (0, {len(data)})',
yaxis_title='Dato filtrado'
)
fig.show()
# Aplicamos filtro pasa bajos
lp_data = apply_filter(n180_data, filter_name='low-pass')
lp_data
x = np.linspace(0, len(data), len(data) + 1)
y = lp_data
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=x,
y=y,
mode='lines',
name='lines'
)
)
fig.update_layout(
title='Datos con filtro pasa bajos',
xaxis_title=f'Rango en (0, {len(data)})',
yaxis_title='Dato filtrado'
)
fig.show()
# Guardamos datos filtrados
save_data('PRUEBAS 24-03-2020/1-REPOSO/JUAN MANUEL/1585082913593_filtered.txt', lp_data)